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Let me be blunt: Shannon would tell us that time forecasting is bullshit: There is infinitely more entropy in the real world out there than any model can even

Let me be blunt: Shannon would tell us that time forecasting is bullshit: There is infinitely more entropy in the real world out there than any model can even remotely capture. The world is not minecraft.

mikkom 7mo ago
Yeah all weather forecasts are just magic
- eru 7mo ago
  And JPG doesn't work either..
- kgwgk 7mo ago
  Whether forecasting is simple: it either rains or it doesn’t. 50/50 probability!
- tgv 7mo ago
  Weather forecasts are notoriously iffy, and accuracy drops with time, but we understand the physics behind it (to a large extent). There's also a lot of fine-grained data available. For some arbitrary time series, there's only one data sequence, and the model is unknown. Extrapolation then becomes a lot more magical.
FartyMcFarter 7mo ago
> Shannon would tell us that time forecasting is bullshit If you're trying to forecast random data, then yes, it's bullshit. Otherwise you have a chance.
- GTP 7mo ago
  But, if you don't have the information required for a forecast, then the outcome can look random. We know the physics needed to predict the outcome of a dice throw, but, since to predict the outcome you would need a lot of information that you don't have, the output is random to you.
drzaiusx11 7mo ago
Time series forecasting has proven useful in a number of different domains from weather to health monitoring. Sure you can easily over fit on the training data, but in general that's a data source/input problem where you need many high quality data sources to find the signal in the noise. The world is chaotic sure, but there are still truths to be found in noisy time series data; saying that the world is too random to be knowable is a bit dismissive, no?
- jdthedisciple 7mo ago
  I agree when it comes to highly niche applications with a generous SNR. Universal models though? And I haven't even mentioned the fact that en mass forecasting ITSELF may influence the subject of forecasting.
  - drzaiusx11 7mo ago
    From my experience, modeling and prediction methods are fairly standardized, involving the same maths across domains by leveraging linear regressions, etc. I see no reason why a model trained on these processes couldn't simply apply those same standard techniques. Math generally is universal after all.
bwfan123 7mo ago
> time forecasting is bullshit for a model to be useful, it doesnt need to capture the behavior of a system. It only needs to capture signals which can be useful. For example, for a biased coin toss, a model is already useful if it can predict a little better than random.

mikkom 7mo ago

Yeah all weather forecasts are just magic

eru 7mo ago
And JPG doesn't work either..
kgwgk 7mo ago
Whether forecasting is simple: it either rains or it doesn’t. 50/50 probability!
tgv 7mo ago
Weather forecasts are notoriously iffy, and accuracy drops with time, but we understand the physics behind it (to a large extent). There's also a lot of fine-grained data available. For some arbitrary time series, there's only one data sequence, and the model is unknown. Extrapolation then becomes a lot more magical.

FartyMcFarter 7mo ago

> Shannon would tell us that time forecasting is bullshit If you're trying to forecast random data, then yes, it's bullshit. Otherwise you have a chance.

GTP 7mo ago
But, if you don't have the information required for a forecast, then the outcome can look random. We know the physics needed to predict the outcome of a dice throw, but, since to predict the outcome you would need a lot of information that you don't have, the output is random to you.

drzaiusx11 7mo ago

Time series forecasting has proven useful in a number of different domains from weather to health monitoring. Sure you can easily over fit on the training data, but in general that's a data source/input problem where you need many high quality data sources to find the signal in the noise. The world is chaotic sure, but there are still truths to be found in noisy time series data; saying that the world is too random to be knowable is a bit dismissive, no?

jdthedisciple 7mo ago
I agree when it comes to highly niche applications with a generous SNR. Universal models though? And I haven't even mentioned the fact that en mass forecasting ITSELF may influence the subject of forecasting.
- drzaiusx11 7mo ago
  From my experience, modeling and prediction methods are fairly standardized, involving the same maths across domains by leveraging linear regressions, etc. I see no reason why a model trained on these processes couldn't simply apply those same standard techniques. Math generally is universal after all.

bwfan123 7mo ago

> time forecasting is bullshit for a model to be useful, it doesnt need to capture the behavior of a system. It only needs to capture signals which can be useful. For example, for a biased coin toss, a model is already useful if it can predict a little better than random.